Evidence map›Paper›PMID 42836117›Full record

ReviewNational science review2026

Emerging integrated photonic neural network technologies for artificial intelligence: from devices to systems.

Changhao Han, Qipeng Yang, Jun Qin, Mingjin Wang, Zhaozheng Yi, Mingxiao Li, Ye Tian, Shuhan Yue, Xueling Wei, Zenan Wu and 7 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in National science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Changhao HanDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA.
Qipeng YangState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, China.
Jun QinCenter for Target Cognition Information Processing Science and Technology, Beijing Information Science and Technology University, Beijing 100192, China.
Mingjin WangLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, CAS, Beijing 100083, China.
Zhaozheng YiLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, CAS, Beijing 100083, China.
Mingxiao LiDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA.
Ye TianState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, China.
Shuhan YueState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, China.
Xueling WeiState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, China.
Zenan WuState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, China.
Liuyan HanDepartment of Fundamental Network Technology, China Mobile Research Institute, Beijing 100053, China.
Dechao ZhangDepartment of Fundamental Network Technology, China Mobile Research Institute, Beijing 100053, China.
Jingxuan ChenLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, CAS, Beijing 100083, China.
Haowen ShuState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, China.
Xingjun WangState Key Laboratory of Photonics and Communications, School of Electronics, Peking University, Beijing 100871, China.
Wanhua ZhengLaboratory of Solid State Optoelectronics Information Technology, Institute of Semiconductors, CAS, Beijing 100083, China.
John E BowersDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid expansion of artificial intelligence (AI) has created an explosive demand for computing power, while traditional von Neumann-based electronic computing faces the memory and interconnect bottlenecks. By migrating critical operations to the optical domain, photonic neural networks (PNNs) offer a promising solution to significantly reduce system latency and energy consumption. Driven by silicon photonics, on-chip PNNs achieve high-density integration on scalable platforms, promoting the evolution of optoelectronic integration systems. This paper presents a comprehensive review of emerging integrated PNN technology for AI, employing a cross-layer framework that spans from fundamental physical devices to system-level implementation. Based on the requirements of PNNs, this review analyzes key hardware devices and mainstream computing core architectures, evaluates integration strategies and discusses advanced system packaging. Furthermore, the review provides a prospect on the field by identifying the remaining technical challenges and highlighting the transformative potential of PNNs as the infrastructure for next-generation AI.

Indexed as

artificial intelligenceoptoelectronic integrationphotonic computingphotonic neural networkssilicon photonics

Identifiers

PMID42836117
PMCPMC13637631

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.